Chaotic Cooperative Particle Swarm Optimization Based on Tent Map

In this paper, a chaotic cooperative particle swarm optimization based on tent map (TCCPSO) is proposed. The cooperative particle swarm optimization (CPSO), can significantly improve the performance of the original algorithm. However, CPSO has the defect of leading to pseudominimizer, which can not be easily escaped by interleaving the CPSO and PSO algorithm. Therefore, we take full advantages of the characteristics of ergodicity and randomness of chaotic variables to help the particles escape from pseudominimizer and converge to global optimum. The experimental results on some benchmark functions show that TCCPSO outperforms the CPSO. In addition, simulation results show that algorithm computation precision is increased and that the algorithm convergence is improved.